22BT

Technical details

Show code

library(GeoPressureR)
library(leaflet)
library(leaflet.extras)
library(raster)
library(dplyr)
library(ggplot2)
library(kableExtra)
library(plotly)
library(GeoLocTools)
setupGeolocation()
knitr::opts_chunk$set(echo = FALSE)
load(paste0("../data/1_pressure/", params$gdl_id, "_pressure_prob.Rdata"))
load(paste0("../data/2_light/", params$gdl_id, "_light_prob.Rdata"))
load(paste0("../data/3_static/", params$gdl_id, "_static_prob.Rdata"))
load(paste0("../data/4_basic_graph/", params$gdl_id, "_basic_graph.Rdata"))

Settings used

All the results produced here are generated with (1) the raw geolocator data, (2) the labeled files of pressure and light and (3) the parameters listed below.

Show code

kable(gpr) %>% scroll_box(width = "100%")

include	gdl_id	crop_start	crop_end	thr_dur	extent_N	extent_W	extent_S	extent_E	map_scale	map_max_sample	map_margin	prob_map_s	prob_map_thr	shift_k	calib_lon	calib_lat	calib_1_start	calib_1_end	calib_2_start	calib_2_end	calib_2_lon	calib_2_lat	prob_light_w	thr_prob_percentile	thr_gs	RingNo	scientific_name	common_name	mass	wing_span	Color
TRUE	22BT	1900-01-01	2100-01-01	12	50	20	-35	120	2	300	30	1	0.9	21600	110.83	48.57	2018-07-15	2018-08-26	NA	NA	NA	NA	0.1	0.9	120	NA	NA	Eurasian Nightjar	NA	NA	NA

Pressure timeserie

The labeling of pressure data is illustrated with this figure. The black dots indicates the pressure datapoint not considered in the matching. Each stationay period is illustrated by a different colored line.

Show code

pressure_na <- pam$pressure %>%
  mutate(obs = ifelse(isoutliar | sta_id == 0, NA, obs))
p <- ggplot() +
  geom_line(data = pam$pressure, aes(x = date, y = obs), colour = "grey") +
  geom_point(data = subset(pam$pressure, isoutliar), aes(x = date, y = obs), colour = "black") +
  # geom_line(data = pressure_na, aes(x = date, y = obs, color = factor(sta_id)), size = 0.5) +
  geom_line(data = do.call("rbind", shortest_path_timeserie) %>% filter(sta_id>0), aes(x = date, y = pressure0, col = factor(sta_id))) +
  theme_bw() +
  scale_colour_manual(values = col) +
  scale_y_continuous(name = "Pressure(hPa)")

ggplotly(p, dynamicTicks = T) %>% layout(showlegend = F)

Light

See geolocator manual for more information on this figure

Show code

raw_geolight <- pam$light %>%
  transmute(
    Date = date,
    Light = obs
  )
lightImage(tagdata = raw_geolight, offset = gpr$shift_k/60/60)
tsimagePoints(twl$twilight,
  offset = gpr$shift_k/60/60, pch = 16, cex = 1.2,
  col = ifelse(twl$deleted, "grey20", ifelse(twl$rise, "firebrick", "cornflowerblue"))
)
abline(v = gpr$calib_2_start, lty = 1, col = "firebrick", lwd = 1.5)
abline(v = gpr$calib_1_start, lty = 1, col = "firebrick", lwd = 1.5)
abline(v = gpr$calib_2_end, lty = 2, col = "firebrick", lwd = 1.5)
abline(v = gpr$calib_1_end, lty = 2, col = "firebrick", lwd = 1.5)

The histogram of zenith angle at the calibration site together with the fitted kernel density. Note that we use a large bandwidth of the kernel to account of the bias of the calibration site.

Show code

hist(z, freq = F)
lines(fit_z, col = "red")

The probability map resulting from light data alone can be seen below.

Show code

li_s <- list()
l <- leaflet(width = "100%") %>%
  addProviderTiles(providers$Stamen.TerrainBackground) %>%
  addFullscreenControl()
for (i_r in seq_len(length(light_prob))) {
  i_s <- metadata(light_prob[[i_r]])$sta_id
  info <- pam$sta[pam$sta$sta_id == i_s, ]
  info_str <- paste0(i_s, " | ", info$start, "->", info$end)
  li_s <- append(li_s, info_str)
  l <- l %>% addRasterImage(light_prob[[i_r]], opacity = 0.8, colors = "OrRd", group = info_str)
}
l %>%
  addCircles(lng = gpr$calib_lon, lat = gpr$calib_lat, color = "black", opacity = 1) %>%
  addLayersControl(
    overlayGroups = li_s,
    options = layersControlOptions(collapsed = FALSE)
  ) %>%
  hideGroup(tail(li_s, length(li_s) - 1))

Light vs Pressure

We can compare light and pressure location at long stationary stopover (>5 days). By assuming the best match of the pressure to be the truth, we can plot the histogram of the zenith angle and compare to the fit of kernel density at the calibration site.

Show code

 raw_geolight <- pam$light %>%
    transmute(
      Date = date,
      Light = obs
    )
 dur <- unlist(lapply(pressure_prob, function(x) difftime(metadata(x)$temporal_extent[2],metadata(x)$temporal_extent[1], units = "days" )))
  long_id <- which(dur>5)

  par(mfrow = c(2, 3))
  for (i_s in long_id){
    twl_fl <- twl %>%
      filter(!deleted) %>%
      filter(twilight>shortest_path_timeserie[[i_s]]$date[1] & twilight<tail(shortest_path_timeserie[[i_s]]$date,1))
    sun <-  solar(twl_fl$twilight)
    z_i <- refracted(zenith(sun, shortest_path_timeserie[[i_s]]$lon[1], shortest_path_timeserie[[i_s]]$lat[1]))
    hist(z_i, freq = F, main = paste0("sta_id=",i_s, " | ",nrow(twl_fl),"twls"))
    lines(fit_z, col = "red")
    xlab("Zenith angle")
  }

Similarly, we can plot the line of sunrise/sunset at the best match of pressure (yellow line) and compare to the raw and labeled light data.

Show code

  lightImage(
    tagdata = raw_geolight,
    offset = gpr$shift_k / 60 / 60
  )
  tsimagePoints(twl$twilight,
                offset = gpr$shift_k / 60 / 60, pch = 16, cex = 1.2,
                col = ifelse(twl$deleted, "grey20", ifelse(twl$rise, "firebrick", "cornflowerblue"))
  )
  for (ts in shortest_path_timeserie){
    twl_fl <- twl %>%
      filter(twilight>ts$date[1] & twilight<tail(ts$date,1))
    tsimageDeploymentLines(twl_fl$twilight,
                           lon = ts$lon[1], ts$lat[1],
                           offset = gpr$shift_k / 60 / 60, lwd = 3,col = adjustcolor("orange", alpha.f = 0.5))
  }

GeoPressureViz

To visualize the path on GeoPressureViz, you will need to also load the pressure and light probability map and align them first with the code below.

Show code

sta_marginal <- unlist(lapply(static_prob_marginal, function(x) raster::metadata(x)$sta_id))
sta_pres <- unlist(lapply(pressure_prob, function(x) raster::metadata(x)$sta_id))
sta_light <- unlist(lapply(light_prob, function(x) raster::metadata(x)$sta_id))
pressure_prob <- pressure_prob[sta_pres %in% sta_marginal]
light_prob <- light_prob[sta_light %in% sta_marginal]

The code below will open with the shortest path computed with the graph approach. You can change it to

Show code

geopressureviz <- list(
  pam_data = pam,
  static_prob = static_prob,
  static_prob_marginal = static_prob_marginal,
  pressure_prob = pressure_prob,
  light_prob = light_prob,
  pressure_timeserie = shortest_path_timeserie
)
save(geopressureviz, file = "~/geopressureviz.RData")

shiny::runApp(system.file("geopressureviz", package = "GeoPressureR"),
  launch.browser = getOption("browser")
)

Stationay period information

Show code

pam$sta %>% kable()

start	end	sta_id
2018-07-15 00:00:00	2018-08-26 12:35:00	1
2018-08-26 19:45:00	2018-08-27 12:40:00	2
2018-08-27 20:35:00	2018-08-28 13:10:00	3
2018-08-28 15:45:00	2018-08-29 13:55:00	4
2018-08-29 14:50:00	2018-08-29 20:30:00	5
2018-08-29 21:20:00	2018-08-30 12:40:00	6
2018-08-30 22:25:00	2018-08-31 12:55:00	7
2018-08-31 21:50:00	2018-09-01 13:45:00	8
2018-09-01 17:35:00	2018-09-02 14:15:00	9
2018-09-02 19:40:00	2018-09-04 14:10:00	10
2018-09-04 23:15:00	2018-09-05 14:20:00	11
2018-09-05 23:15:00	2018-09-06 15:30:00	12
2018-09-06 22:25:00	2018-09-07 15:00:00	13
2018-09-07 23:55:00	2018-09-08 19:40:00	14
2018-09-09 00:20:00	2018-09-09 15:20:00	15
2018-09-09 22:50:00	2018-09-10 15:30:00	16
2018-09-11 00:30:00	2018-09-11 14:10:00	17
2018-09-12 00:40:00	2018-09-12 19:40:00	18
2018-09-12 20:45:00	2018-09-13 14:00:00	19
2018-09-13 14:55:00	2018-09-14 15:00:00	20
2018-09-14 16:40:00	2018-09-15 13:55:00	21
2018-09-15 15:05:00	2018-09-17 00:30:00	22
2018-09-17 00:40:00	2018-09-17 13:55:00	23
2018-09-17 15:25:00	2018-09-17 16:30:00	24
2018-09-17 19:35:00	2018-09-17 22:35:00	25
2018-09-18 00:00:00	2018-09-19 00:35:00	26
2018-09-19 00:50:00	2018-09-19 13:50:00	27
2018-09-19 14:05:00	2018-09-21 00:20:00	28
2018-09-21 00:55:00	2018-10-01 13:35:00	29
2018-10-01 20:15:00	2018-10-02 14:15:00	30
2018-10-03 01:05:00	2018-10-03 14:00:00	31
2018-10-04 01:20:00	2018-10-04 15:20:00	32
2018-10-04 22:50:00	2018-10-05 14:25:00	33
2018-10-06 01:40:00	2018-10-06 14:55:00	34
2018-10-07 01:50:00	2018-10-07 15:10:00	35
2018-10-08 01:55:00	2018-10-08 15:45:00	36
2018-10-08 21:55:00	2018-10-09 15:10:00	37
2018-10-10 00:20:00	2018-10-10 16:10:00	38
2018-10-10 23:55:00	2018-10-11 14:55:00	39
2018-10-11 15:05:00	2018-10-12 01:45:00	40
2018-10-12 02:10:00	2018-10-12 14:55:00	41
2018-10-12 15:15:00	2018-10-17 21:55:00	42
2018-10-17 23:40:00	2018-10-26 00:40:00	43
2018-10-26 02:05:00	2018-10-26 14:50:00	44
2018-10-26 19:30:00	2018-10-27 14:55:00	45
2018-10-27 20:05:00	2018-10-28 18:35:00	46
2018-10-29 02:20:00	2018-10-29 15:10:00	47
2018-10-29 15:30:00	2018-10-30 18:50:00	48
2018-10-30 22:40:00	2018-10-31 21:45:00	49
2018-10-31 22:25:00	2018-11-01 21:05:00	50
2018-11-01 21:25:00	2018-11-16 15:25:00	51
2018-11-16 19:20:00	2018-11-19 17:05:00	52
2018-11-19 18:55:00	2018-11-21 16:00:00	53
2018-11-21 23:25:00	2018-11-22 15:35:00	54
2018-11-23 02:45:00	2018-11-23 20:50:00	55
2018-11-24 02:45:00	2018-11-24 16:45:00	56
2018-11-25 02:45:00	2018-11-27 17:05:00	57
2018-11-28 02:45:00	2018-11-28 17:25:00	58
2018-11-28 22:10:00	2018-11-29 18:25:00	59
2018-11-29 19:00:00	2018-11-30 02:20:00	60
2018-11-30 02:35:00	2018-11-30 16:30:00	61
2018-12-01 02:10:00	2018-12-01 21:50:00	62
2018-12-02 02:00:00	2018-12-02 18:35:00	63
2018-12-03 02:20:00	2018-12-03 18:45:00	64
2018-12-04 03:10:00	2018-12-04 19:10:00	65
2018-12-05 02:30:00	2018-12-05 19:55:00	66
2018-12-05 20:25:00	2019-04-01 16:50:00	67
2019-04-02 03:30:00	2019-04-02 16:50:00	68
2019-04-03 00:35:00	2019-04-03 16:45:00	69
2019-04-04 03:10:00	2019-04-04 17:05:00	70
2019-04-05 00:20:00	2019-04-05 17:35:00	71
2019-04-05 23:30:00	2019-04-06 18:55:00	72
2019-04-07 03:15:00	2019-04-07 17:10:00	73
2019-04-08 00:15:00	2019-04-08 16:55:00	74
2019-04-09 02:50:00	2019-04-09 17:10:00	75
2019-04-09 19:40:00	2019-04-09 23:55:00	76
2019-04-10 02:55:00	2019-04-10 17:55:00	77
2019-04-10 18:50:00	2019-04-10 20:50:00	78
2019-04-10 21:45:00	2019-04-11 00:55:00	79
2019-04-11 02:40:00	2019-04-11 20:35:00	80
2019-04-12 02:45:00	2019-04-17 16:35:00	81
2019-04-18 02:50:00	2019-04-18 16:30:00	82
2019-04-19 01:30:00	2019-04-19 20:25:00	83
2019-04-19 22:40:00	2019-04-22 17:05:00	84
2019-04-23 04:10:00	2019-04-24 00:35:00	85
2019-04-24 02:55:00	2019-04-29 00:50:00	86
2019-04-29 01:20:00	2019-04-29 16:35:00	87
2019-04-29 20:35:00	2019-04-30 00:10:00	88
2019-04-30 02:25:00	2019-04-30 15:35:00	89
2019-05-01 02:20:00	2019-05-01 16:05:00	90
2019-05-02 02:05:00	2019-05-02 15:30:00	91
2019-05-03 02:00:00	2019-05-03 15:30:00	92
2019-05-04 02:00:00	2019-05-04 15:20:00	93
2019-05-05 01:55:00	2019-05-05 15:25:00	94
2019-05-06 01:45:00	2019-05-06 15:40:00	95
2019-05-07 01:35:00	2019-05-07 15:10:00	96
2019-05-08 01:15:00	2019-05-08 15:50:00	97
2019-05-09 01:00:00	2019-05-09 16:00:00	98
2019-05-09 23:30:00	2019-05-10 16:00:00	99
2019-05-11 00:45:00	2019-05-11 15:05:00	100
2019-05-11 15:10:00	2019-05-12 20:25:00	101
2019-05-12 20:50:00	2019-05-13 18:25:00	102
2019-05-13 19:30:00	2019-05-14 20:50:00	103
2019-05-15 00:35:00	2019-05-15 15:45:00	104
2019-05-16 00:10:00	2019-05-16 14:55:00	105
2019-05-16 15:35:00	2019-05-17 15:05:00	106
2019-05-17 16:10:00	2019-05-18 15:05:00	107
2019-05-18 15:40:00	2019-05-19 15:10:00	108
2019-05-19 15:35:00	2019-05-20 16:00:00	109
2019-05-20 18:05:00	2019-05-21 15:10:00	110
2019-05-21 15:30:00	2019-05-22 15:45:00	111
2019-05-23 00:10:00	2019-05-23 15:00:00	112
2019-05-23 23:50:00	2019-05-24 15:40:00	113
2019-05-24 23:30:00	2019-05-25 15:10:00	114
2019-05-25 23:10:00	2019-05-26 15:55:00	115
2019-05-26 23:00:00	2019-05-27 16:05:00	116
2019-05-27 22:45:00	2019-05-28 14:55:00	117
2019-05-28 15:35:00	2019-05-29 16:20:00	118
2019-05-29 22:40:00	2019-05-30 15:30:00	119
2019-05-30 22:05:00	2019-05-31 06:10:00	120